Single Leaf Research Articles

EFFECT OF NANO AND CHEMICAL FERTILIZATION WITH NPK AND CHELATED ZINC ON VEGETATIVE GROWTH OF TAIFI AND KAMALI GRAPE CULTIVARS

This research was performed during the 2021 seasons in private orchard located in Bara-Bhar-Duhok governorate to study the effect of soil application of chemical and Nano-NPK, foliar spray with chelated Nano-Zinc and their interaction on vegetative growth of Taifi and Kamali grape cultivars (Vitis vinifera L.). NPK (20:20:20) was added to the soil at five levels (0, 216 g.vine-1, 324 g.vine-1 chemical NPK, 3.24 and 6.48 g.vine-1 Nano-NP (, chelated Nano-Zinc was added at three concentration (0, 100 and 200 mg.l-1). The results showed the superiority of the Taifi cultivar in traits of number of leaves per vine, Single leaf Area and leaves dry weight over the Kamali cultivar, while Kamali cultivar was superiority in traits of leaf area per cluster ratio and total chlorophyll over the Taifi cultivar. Soil application of chemical and Nano-NPK and foliar application of Nano-zinc fertilizer achieved significant increase in vegetative growth (number of leaves, Single leaf Area, and total leaves area per vine, total chlorophyll and leaves dry weight) compare to control. Furthermore, combination among high concentration of Nano-NPK, Nano-zinc for Taifi and Kamali cultivars improved all parameters in comparison with the control.

The Impact of Short-Term Drought on the Photosynthetic Characteristics and Yield of Peanuts Grown in Saline Alkali Soil

Peanuts grown in saline alkali soil are also subjected to drought stress caused by water scarcity. Therefore, we used HY25 (peanut variety) as an experimental material to investigate the effects of drought on the height of peanut main stems, length of the first lateral branch, leaf area per plant, SPAD value, net photosynthetic rate, and accumulation and distribution of photosynthetic products in saline alkali soil. The results showed that the combined stress of short-term drought and salt significantly reduced the main stem height, first lateral branch length, single plant leaf area, SPAD value, net photosynthetic rate (Pn), intercellular carbon dioxide concentration (Ci), and dry matter accumulation of peanuts, including a decrease in single plant pod yield, 100-pod weight, 100-kernel weight, and peanut yield. And the impact of drought stress on peanut yield varies at different growth stages. For example, under drought stress alone, the sensitive period is the 40th day after planting (40D) > 60th day after planting (60D) > 30th day after planting (30D). Short-term drought has the greatest impact on peanut yield at 40D, while in contrast, resuming watering after drought at 30D results in a slight but not significant increase in peanut yield in comparison with the control. Under the combined stress of drought and salt, the sensitive period of peanuts was 40D > 30D > 60D, and the single pod weight of peanuts was significantly reduced by 15.26% to 57.60% from the flowering stage to the pod stage under drought treatment compared to salt treatment, indicating a significant interaction between drought and salt stress, reducing the single leaf area and net photosynthetic rate of peanut leaves, ultimately leading to a decrease in peanut yield. Therefore, when planting peanuts in saline alkali soil, drought should be avoided, especially early drought, in order to prevent the combined effects of drought and salt stress from harming peanut yield.

Loaded Vibration Characteristics of Large Statically Deformed Single Parabolic Leaf Spring

Loaded Vibration Characteristics of Large Statically Deformed Single Parabolic Leaf Spring

Change in functional trait diversity mediates the effects of nutrient addition on grassland stability

Abstract Nutrient enrichment impacts grassland plant diversity such as species richness, functional trait composition and diversity, but whether and how these changes affect ecosystem stability in the face of increasing climate extremes remains largely unknown. We quantified the direct and diversity‐mediated effects of nutrient addition (by nitrogen, phosphorus, and potassium) on the stability of above‐ground biomass production in 10 long‐term grassland experimental sites. We measured five facets of stability as the temporal invariability, resistance during and recovery after extreme dry and wet growing seasons. Leaf traits (leaf carbon, nitrogen, phosphorus, potassium, and specific leaf area) were measured under ambient and nutrient addition conditions in the field and were used to construct the leaf economic spectrum (LES). We calculated functional trait composition and diversity of LES and of single leaf traits. We quantified the contribution of intraspecific trait shifts and species replacement to change in functional trait composition as responses to nutrient addition and its implications for ecosystem stability. Nutrient addition decreased functional trait diversity and drove grassland communities to the faster end of the LES primarily through intraspecific trait shifts, suggesting that intraspecific trait shifts should be included for accurately predicting ecosystem stability. Moreover, the change in functional trait diversity of the LES in turn influenced different facets of stability. That said, these diversity‐mediated effects were overall weak and/or overwhelmed by the direct effects of nutrient addition on stability. As a result, nutrient addition did not strongly impact any of the stability facets. These results were generally consistent using individual leaf traits but the dominant pathways differed. Importantly, major influencing pathways differed using average trait values extracted from global trait databases (e.g. TRY). Synthesis. Investigating changes in multiple facets of plant diversity and their impacts on multidimensional stability under global changes such as nutrient enrichment can improve our understanding of the processes and mechanisms maintaining ecosystem stability.

QTL mapping for branch- and leaf-related traits with a high-density SNP genetic map in litchi (Litchi chinensis Sonn.)

Litchi (Litchi chinensis Sonn.), an important fruit tree in tropical and subtropical regions, possesses substantial economic value. The branch- and leaf-related traits of litchi have a significant impact on litchi yield and quality. However, due to limitations such as the density of the genetic linkage map, there have been few studies on mapping QTLs of branch- and leaf-related traits. In this study, a high-density genetic map was constructed by next-generation sequencing (NGS) using an F1 population of 264 progenies, derived from the cross between the cultivars ‘Sanyuehong’ and ‘Ziniangxi’. A total of 2574 high-quality BINs (binomial intervals) were obtained, and a genetic linkage map was constructed with a total length of 1753.3 cM and an average marker distance of 0.68 cM. With the genetic map and the phenotyping of single leaf length (SLL), single leaf width (SLW), leaf shape index (LSI), weight of specific leaf (WSL), petiole length (PL) and compound leaf length (CLL) measured in three seasons, 11, 9, 9, 10, 9 and 12 QTLs were detected for SLL, SLW, WSL, LSI, PL and CLL traits, respectively. Among these QTLs, five QTLs were consistently detected in two seasons and 12 pleiotropic QTLs were identified for at least two traits. These findings will provide new insights for the gene cloning for branch- and leaf-related traits as well as marker-assisted selection (MAS).

Residual attention based multi-label learning for apple leaf disease identification

Recent studies suggest that plant disease identification via machine learning approach is vital for preventing the spread of diseases. Identifying multiple diseases simultaneous on a single leaf is one of the most irritating issues in agricultural production. However, the existing approaches are difficult to meet the requirements of production practice in accuracy or interpretability. Here, we present residual attention based multi-label learning framework (RAMDI), a method for predicting apple leaf diseases in natural environment. Built upon an attention based multi-label learning framework, the channel and spatial attention mechanisms are investigated and embedded in residual network for multi-label disease prediction, which takes advantage of channel-wise and spatial-wise attention weights. Experimental results indicate that the RAMDI achieves 0.976 accuracy, 0.986 F-score, and 0.979 mAPs, outperforms the existing state-of-the-art apple leaf disease identification models. RAMDI not only predicts multi-disease on a single leaf simultaneously, but also reveals the interpretability among positive predictions that contribute most to identify the key features that are significant for the leaf diseases. This method achieves the following two achievements. Firstly, it provides a solution for detecting multiple diseases on a single leaf. Secondly, this approach gains an interpretable understanding for apple leaf disease identification.

CTDUNet: A Multimodal CNN-Transformer Dual U-Shaped Network with Coordinate Space Attention for Camellia oleifera Pests and Diseases Segmentation in Complex Environments.

Camellia oleifera is a crop of high economic value, yet it is particularly susceptible to various diseases and pests that significantly reduce its yield and quality. Consequently, the precise segmentation and classification of diseased Camellia leaves are vital for managing pests and diseases effectively. Deep learning exhibits significant advantages in the segmentation of plant diseases and pests, particularly in complex image processing and automated feature extraction. However, when employing single-modal models to segment Camellia oleifera diseases, three critical challenges arise: (A) lesions may closely resemble the colors of the complex background; (B) small sections of diseased leaves overlap; (C) the presence of multiple diseases on a single leaf. These factors considerably hinder segmentation accuracy. A novel multimodal model, CNN-Transformer Dual U-shaped Network (CTDUNet), based on a CNN-Transformer architecture, has been proposed to integrate image and text information. This model first utilizes text data to address the shortcomings of single-modal image features, enhancing its ability to distinguish lesions from environmental characteristics, even under conditions where they closely resemble one another. Additionally, we introduce Coordinate Space Attention (CSA), which focuses on the positional relationships between targets, thereby improving the segmentation of overlapping leaf edges. Furthermore, cross-attention (CA) is employed to align image and text features effectively, preserving local information and enhancing the perception and differentiation of various diseases. The CTDUNet model was evaluated on a self-made multimodal dataset compared against several models, including DeeplabV3+, UNet, PSPNet, Segformer, HrNet, and Language meets Vision Transformer (LViT). The experimental results demonstrate that CTDUNet achieved an mean Intersection over Union (mIoU) of 86.14%, surpassing both multimodal models and the best single-modal model by 3.91% and 5.84%, respectively. Additionally, CTDUNet exhibits high balance in the multi-class segmentation of Camellia oleifera diseases and pests. These results indicate the successful application of fused image and text multimodal information in the segmentation of Camellia disease, achieving outstanding performance.

An overview of floral and vegetative evolution in the Asian clade of Bulbophyllum (Orchidaceae)

Background and aims – Bulbophyllum, the largest genus in Orchidaceae, exhibits a diverse morphology in both reproductive and vegetative characters. While trait diversity and evolution has been extensively studied in Malagasy species and within the Cirrhopetalum alliance clade, the evolution of reproductive and vegetative characters at the whole level of the Asian clade remains largely unexplored. Material and methods – We reconstructed the phylogeny of approximately 11% of all Asian Bulbophyllum species using Bayesian inference and maximum likelihood estimation based on nuclear (ITS) and chloroplast (matK, psbA-trnH) DNA sequence data. This phylogenetic framework allowed us to examine the evolution of two vegetative and four floral characters through ancestral state reconstruction. Key results and conclusion – The ancestral character states of the Asian clade of Bulbophyllum include a single leaf, distinct pseudobulbs, multiple-flowered inflorescences, and lateral and dorsal sepals similar in length. One-leaved pseudobulbs evolved into two-leaved pseudobulbs multiple times. Distinct pseudobulbs gave rise to indistinct pseudobulbs twice. Multiple-flowered inflorescences shifted to solitary flowers and 2–3-flowered inflorescences multiple times, with some instances of evolutionary reversal. Lateral sepal elongation also presents a convergent evolutionary scenario.

TeaPoseNet: A deep neural network for tea leaf pose recognition

The estimation of tea leaf pose is an emerging research topic. Recognising the morphological features of tea leaves can help accurately categorise, grade, and determine their level of maturity. Therefore, this study proposes a deep neural network, TeaPoseNet, to estimate tea leaf poses. The algorithm was trained and validated using a dataset of one-bud-one-leaf images of Yinghong No.9 tea leaves and was compared with four other pose estimation networks. At the same time, the contribution of TKS_NMS to the algorithm was validated through ablation experiments. The results indicate that TKS_NMS improved the EPE accuracy of pose recognition by 16.33 %. More specifically, the algorithm achieved a good overall performance, with PCK, AUC, EPE, and NME reaching 0.9800, 0.8147, 9.0955, and 0.0644, respectively. The average running speed for detecting the pose of a single tea leaf image was 40.01 ms. To the best of our knowledge, this is the first application of pose estimation technology to the detection and analysis of Yinghong No.9 tea leaves. The results show that the proposed algorithm can effectively estimate the pose of tea leaves, thus providing a reference for subsequent tea research.

Decoding stomatal characteristics regulating water use efficiency at leaf and plant scales in rice genotypes.

Stomatal traits in rice genotypes affect water use efficiency. Low-frequency small-size stomata correlate with whole plant efficiency, while low-frequency large-size stomata show intrinsic efficiency and responsiveness to vapour pressure deficit. Leaf surface and the patterning of the epidermal layer play a vital role in determining plant growth. While the surface helps in determining radiation interception, epidermal pattern of stomatal factors strongly regulate gas exchange and water use efficiency (WUE). This study focuses on identifying distinct stomatal traits among rice genotypes to comprehend their influence on WUE. Stomatal frequency ranged from 353 to 687 per mm2 and the size varied between 128.31 and 339.01 μm2 among 150 rice germplasm with significant variability in abaxial and adaxial surfaces. The cumulative water transpired and WUE determined at the outdoor phenomics platform, over the entire crop growth period as well as during specific hours of a 24h-day did not correlate with stomatal frequency nor size. However, genotypes with low-frequency and large-size stomata recorded higher intrinsic water use efficiency (67.04μmol CO2 mol-1 H2O) and showed a quicker response to varying vapour pressure deficit that diurnally ranged between 0.03 and 2.17kPa. The study demonstrated the role of stomatal factors in determining physiological subcomponents of WUE both at single leaf and whole plant levels. Differential expression patterns of stomatal regulatory genes among the contrasting groups explained variations in the epidermal patterning. Increased expression of ERECTA, TMM and YODA genes appear to contribute to decreased stomatal frequency in low stomatal frequency genotypes. These findings underscore the significance of stomatal traits in breeding programs and strongly support the importance of these genes that govern variability in stomatal architecture in future crop improvement programs.

ECOSENSE - Multi-scale quantification and modelling of spatio-temporal dynamics of ecosystem processes by smart autonomous sensor networks

Global climate change threatens ecosystem functioning worldwide. Forest ecosystems are particularly important for carbon sequestration, thereby buffering climate change and providing socio-economic services. However, recurrent stresses, such as heat waves, droughts and floods can affect forests with potential cascading effects on their carbon sink capacity, drought resilience and sustainability. Knowledge about the stress impact on the multitude of processes driving soil-plant-atmosphere interactions within these complex forest systems is widely lacking and uncertainty about future changes extremely high. Thus, forecasting forest response to climate change will require a dramatically improved process understanding of carbon and water cycling across various temporal (minutes to seasons) and spatial (leaf to ecosystem) scales covering atmosphere, biosphere, pedosphere and hydrosphere components. Many relevant processes controlling carbon and water exchange occur at small scales (e.g. rhizosphere, single leaf) with a high spatial and temporal variability, which is poorly constrained. However, interactions and feedback loops can be key players that amplify or dampen a system’s response to stress. Moreover, spatial and temporal scaling rules for these non-linear processes in structurally and functionally diverse ecosystems are unknown. Legacy effects, for example, altered response after previous stress and retarded recovery of forests after climate extremes, are not captured in state-of-the-art models. Currently, we are lacking the appropriate and interconnected measurement, data assimilation and modelling tools allowing for a comprehensive, real-time quantification of key processes at high spatio-temporal coverage in heterogeneous environments. Moreover, since climate impacts are highly unpredictable with respect to timing and location, future research will require novel mobile, easily deployable and cost-efficient approaches. ECOSENSE, therefore, assembles expertise from environmental and engineering sciences, both being excellently paired at the University of Freiburg. Our interdisciplinary research project will investigate all relevant scales in a next-generation ecosystem research assessment (ECOSENSE). Our vision is to detect and forecast critical changes in ecosystem functioning, based on the understanding of hierarchical process interaction. In the first phase, ECOSENSE will explore these process interactions by investigating pools and fluxes of water and carbon, i.e. CO2 exchange, isotope discrimination and volatile organic compounds (VOC), as well as stress indicators by remotely and in situ sensed chlorophyll fluorescence. To address these research tasks, ECOSENSE will develop, implement and test a distributed, autonomous, intelligent sensor network, based on novel microsensors tailored to the specific needs in remote and harsh forest environments. They will measure the spatio-temporal dynamics of ecosystem pools and fluxes in a naturally complex structured forest system with minimal physiological impact. Measured data will be transferred in real-time into a sophisticated database, which will be explored for process analysis, conducted by Artificial Intelligence and close to real-time process-based ecosystem models for now- and forecasting applications. Thereby, ECOSENSE will: i) break new ground for integrative ecosystem research by identifying hierarchies and interactions of abiotic and physiological processes of forest carbon and water exchange, ii) provide a profound understanding of complex ecosystem responses to environmental stressors and iii) enable the prediction of process-based alterations in ecosystem functioning and sustainability. Our novel ECOSENSE toolkit, tested and validated in controlled climate extreme experiments and our ECOSENSE Forest, will open new horizons for rapid assessment in vast and remote ecosystems. Thereby, ECOSENSE will allow for a unique avenue of data acquisition and, consequently, for unprecedented scale-crossing ecosystem understanding and modelling.

First Report of Diaporthe tectonendophytica Causing Leaf Spot on Dalbergia odorifera in Guangxi, China.

Dalbergia odorifera T. Chen (Family: Fabaceae) is a national level II protected plant in China, with extremely high economic value and medical properties (Zhao et al. 2020). In June 2023, an unknown leaf spot was found in a garden land of Pingxiang city, Guangxi, China, and approximately 80% of the plants covered an area of 500 m2 displayed similar symptoms. The spots were grey to white, 4~6 mm in diameter (n=30) with black pycnida on the spots surface (Fig S1, A-D). Multiple disease spots were observed on a single leaf. The pycnida on the lesion were picked and mashed, to make a conidia suspension using sterile water. The conidial solution was then spread onto a potato dextrose agar (PDA) plate containing streptomycin, with 10 mg of streptomycin per 100 mL, and incubated for 3 days at 28°C with a 12 hour photoperiod. Three isolates (GXPX01, GXPX02 and GXPX03) were obtained by re-culturing the colonies on fresh PDA plates. The colony on PDA were white with aerial mycelia (Fig S1, E-F). Black conidiomata developed at 28°C with a 12 hour photoperiod in 20 days (Fig S1, G-H). Alpha conidia were 4.2~6.4 μm × 1.8~2.6 μm (average =5.1 × 2.3 μm, n = 30), mostly bi-guttulate, hyaline, ellipsoid, apex bluntly rounded, base obtuse to subtruncate, smooth (Fig S1, I). Beta conidia were 15.1~33.5 μm × 1~1.8 μm (average = 24.5 × 1.5 μm, n = 30), filiform, hyaline, curved or hamate, aseptate, base subtruncate (Fig S1, J). Morphological characteristics of the three isolates matched those of Diaporthe spp.(Gomes et al. 2013). The rDNA internal transcribed spacer (ITS) region, the translation elongation factor 1-α (TEF1), the calmodulin (CAL), the histone H3 (HIS) and the β-tubulin (TUB2) genes of the three isolates were amplified using the primer pairs ITS4/ITS5, EF1-728F/EF1-986R, CAL-228F/CAL2Rd, CYLH3F/H3-1B, and T1 /CYLTUB1R, respectively (Crous et al. 2004, Sun et al. 2021). The sequences were all deposited in GenBank (accession numbers OR437511 to OR437513 for ITS, OR454965 to OR454967 for TEF1, OR454968 to OR454970 for CAL, OR454971 to OR454973 for TUB2, OR454974 to OR454976 for H3). Sequences had 98.36% to 100% homology with the corresponding sequences of known Diaporthe tectonendophytica strains MFLUCC 13-0471 in the NCBI database. Phylogenetic analysis was based on combined ITS, TEF1, TUB2 and CAL sequences data using MEGA 11 software to construct phylogenetic tree with Maximum Likelihood (Doilom et al. 2017). In the phylogenetic tree, the combined sequences attributed the three isolates to the D. tectonendophytica (Fig S2). The pathogenicity was tested on leaves of 1.5-year-old D. odorifera seedlings. Three leaves were wounded with a sterile needle and individually inoculated with a 5 mm mycelial disk of PDA culture from each isolate. Sterile PDA disks inoculated leaves as a control. The test was repeated three times. The inoculated plants were placed in a greenhouse at 25℃ and 90% humidity, with a photoperiod of 12 hours. Five days after inoculation, necrotic lesions appeared on inoculated leaves and symptoms from all three isolates were the same as those form natural infections ( Fig S1, K-N), whereas all the control remained symptomless (Fig S1, P). The pathogen was reisolated from the inoculated leaves and again identified as D. tectonendophytica, with the same methodology used for the initial identification. D. tectonendophytica was reported to cause plant diseases, such as stem gray blight of red-fleshed dragon fruit (Hylocereus polyrhizus) (Rahim et al. 2021), leaf spots disease on Elaeagnus conferta and Pometia pinnata (Sun et al. 2021). To our knowledge, this is the first report of D. ctonendophytica causing leaf spot disease on D. odorifera.

Experiment and analysis of physical, mechanical, and viscoelastic properties of the roots and stalks of green leafy vegetables.

Green leafy vegetables are an essential component of Chinese leafy vegetables. Due to their crisp stems and tender leaves, orderly harvester generally causes significant mechanical clamping damage. The physical and mechanical properties of green leafy vegetables are one of the important basis to design the orderly harvester. At the same time, they provide important parameters for the simulation and optimization of harvester. So, this paper measured the physical characteristic parameters of roots and stems of green leafy vegetables. Then, based on the TMS-Pro texture analyzer, the elasticity modulus of the roots and stems of green leafy vegetables were measured. The static friction coefficient, dynamic friction coefficient, and restitution coefficient of green leafy vegetables root-root, stem-stem, root-steel, and stem-steel were measured separately using a combination method of inclined plane and high-speed photography. Uniaxial compression creep experiments were carried out on whole and single leaf of green leafy vegetables using the TA.XT plus C universal testing machine. The constitutive equation of the four-element Burgers model was used to fit the deformation curve of the sample with time during the constant-pressure loading stage. The fitting determination coefficients R2 were all higher than 0.996, which verified the reasonable validity of the selected model. The above experimental results provide a parameter basis and theoretical support for the design and discrete element simulation optimization of orderly harvester critical components of green leafy vegetables.

Multifactorial Tomato Leaf Disease Detection Based on Improved YOLOV5

Target detection algorithms can greatly improve the efficiency of tomato leaf disease detection and play an important technical role in intelligent tomato cultivation. However, there are some challenges in the detection process, such as the diversity of complex backgrounds and the loss of leaf symmetry due to leaf shadowing, and existing disease detection methods have some disadvantages in terms of deteriorating generalization ability and insufficient accuracy. Aiming at the above issues, a target detection model for tomato leaf disease based on deep learning with a global attention mechanism, TDGA, is proposed in this paper. The main idea of TDGA includes three aspects. Firstly, TDGA adds a global attention mechanism (GAM) after up-sampling and down-sampling, as well as in the SPPF module, to improve the feature extraction ability of the target object, effectively reducing the interference of invalid targets. Secondly, TDGA uses a switchable atrous convolution (SAConv) in the C3 module to improve the model’s ability to detect. Thirdly, TDGA adopts the efficient IoU loss (EIoU) instead of complete IoU loss (CIoU) to solve the ambiguous definition of aspect ratio and sample imbalance. In addition, the influences of different environmental factors such as single leaf, multiple leaves, and shadows on the performance of tomato disease detection are extensively experimented with and analyzed in this paper, which also verified the robustness of TDGA. The experimental results show that the average accuracy of TDGA reaches 91.40%, which is 2.93% higher than that of the original YOLOv5 network, which is higher than YOLOv5, YOLOv7, YOLOHC, YOLOv8, SSD, Faster R-CNN, RetinaNet and other target detection networks, so that TDGA can be utilized for the detection of tomato leaf disease more efficiently and accurately, even in complex environments.

Rainfall interception by leaf litters: What happens with fallen leaves of different types and mixing degrees under simulated rainfall?

As a crucial component within the vertical structure of the forests, leaf litter plays a significant role in the hydrological effects of the forest ecosystem. Studying the impact of different types of leaf litter combinations on the hydrological performance, as well as understanding how the ratio of leaf types in the litter layer influences it, are crucial for achieving a deeper comprehension of the role of the litter layer in forest hydrological processes. The objectives of this study were to determine the rainfall interception characteristics of single leaf litters with different types (Pinus massoniana, Cinnamomum camphora and Magnolia grandiflora) and mixing degrees (P. massoniana × C. camphora, P. massoniana × M. grandiflora, and C. camphora × M. grandiflora, with mixing ratios of 1:3, 1:1, and 3:1) under various slope gradients (0°, 5° and 25°). The results showed that with varying combinations of leaf litters and different mixing degrees, there existed certain disparities in runoff mitigation efficiency. In particular, the response time of the P. massoniana × M. grandiflora to rainfall gradually advanced as the proportion of P. massoniana increased. The maximum and minimum interception capacity of the leaf litters were both highest for C. camphora, and minimum interception capacity of the mixed litters of P. massoniana × C. camphora decreased with an increasing proportion of P. massoniana. When C. camphora accounted for a larger proportion in the mixed litters of C. camphora × M. grandiflora, the minimum interception capacity was larger. In the combination of P. massonian × C. camphora, the proportion of infiltration flow was the highest when the mixing ratio was 1:1, while that of slope and lateral flow was the lowest. On the sloped land, the infiltration flow of P. massoniana × M. grandiflora increased with the increasing proportion of P. massoniana, while the slope flow was opposite. Mixtures of litters affect the accumulation spatial structure of the litter layer, consequently altering the interception and redistribution characteristics of rainfall. The research provides a reliable foundation for objectively assessing the water transfer mechanism and conservation function of the litter layer.

Loss-of-function of PGL10 impairs photosynthesis and tolerance to high-temperature stress in rice.

High temperature (HT) affects the production of chlorophyll (Chl) pigment and inhibits cellular processes that impair photosynthesis, and growth and development in plants. However, the molecular mechanisms underlying heat stress in rice are not fully understood yet. In this study, we identified two mutants varying in leaf color from the ethylmethanesulfonate mutant library of indica rice cv. Zhongjiazao-17, which showed pale-green leaf color and variegated leaf phenotype under HT conditions. Mut-map revealed that both mutants were allelic, and their phenotype was controlled by a single recessive gene PALE GREEN LEAF 10 (PGL10) that encodes NADPH:protochlorophyllide oxidoreductase B, which is required for the reduction of protochlorophyllide into chlorophyllide in light-dependent tetrapyrrole biosynthetic pathway-based Chl synthesis. Overexpression-based complementation and CRISPR/Cas9-based knockout analyses confirmed the results of Mut-map. Moreover, qRT-PCR-based expression analysis of PGL10 showed that it expresses in almost all plant parts with the lowest expression in root, followed by seed, third leaf, and then other green tissues in both mutants, pgl10a and pgl10b. Its protein localizes in chloroplasts, and the first 17 amino acids from N-terminus are responsible for signals in chloroplasts. Moreover, transcriptome analysis performed under HT conditions revealed that the genes involved in the Chl biosynthesis and degradation, photosynthesis, and reactive oxygen species detoxification were differentially expressed in mutants compared to WT. Thus, these results indicate that PGL10 is required for maintaining chloroplast function and plays an important role in rice adaptation to HT stress conditions by controlling photosynthetic activity.

Studies on the Variability of Indigenous Mulberry Germplasm for Growth and Yield Traits

Twenty two indigenous mulberry accessions were evaluated for growth and yield traits in three seasons. ANOVA on growth and yield showed significant variation among the accessions for the traits. The interaction between accession and season was highly significant for all traits. The co-efficient of variation was maximum for single leaf weight (17.08%) and minimum for total shoot length (0.08%). The divergence analysis grouped 22 indigenous mulberry accessions into 5 clusters. Maximum accessions were grouped in clusters I & II (8 acc.) followed by cluster V (4 acc.). The diversity among the accessions measured by inter-cluster distance (D2) showed variation. The cluster group indicates that mulberry accessions were distributed in different clusters irrespective of geographical distribution.

Use of an automatic fruit-zone cooling system to cope with multiple summer stresses in Sangiovese and Montepulciano grapes.

Grapevines are frequently subjected to heatwaves and limited water availability during ripening. These conditions can have consequences for the physiological health of the vines. Moreover, the situation is often exacerbated by intense solar radiation, resulting in reduced yield due to sunburn and a decline in quality. In light of these challenges, our study aimed to develop a fruit-zone cooling system designed to mitigate grape sunburn damage and improve the microclimate conditions within the vineyard. The system comprises a network of proximal sensors that collect microclimate data from the vineyard and an actuator that activates nebulizers when the temperature exceeds the threshold of 35°C. The research was conducted over two years (2022 and 2023) in Bologna (Italy) using potted Sangiovese and Montepulciano vines. These two vintages were characterized by high temperatures, with varying amounts of rainfall during the test period, significantly impacting the evaporative demand, which was notably higher in 2023. Starting from the veraison stage we compared three treatments: Irrigated control vines (WW); Control vines subjected to 50% water restriction during the month of August (WS); WS vines treated with nebulized water in the bunch area during the stress period (WS+FOG). The application of nebulized water effectively reduced the temperature of both the air around the clusters and the clusters themselves. As we expected, Montepulciano showed better single leaf assimilation rate and stomatal conductance under non-limiting water conditions than Sangiovese while their behavior was unaffected under water-scarce conditions. Importantly, for the first time, we demonstrated that nebulized water positively affected gas exchange in both grape varieties. In addition to this, the vines treated with the misting system exhibited higher productivity compared to WS vines without affecting technological maturity. In the 2023 vintage, the activation of the system prevented the ripening blockage that occurred in Montepulciano under water stress. Regarding the concentration of total anthocyanins, a significant increase in color was observed in WS+FOG treatment, suggesting a predominant role of microclimate on anthocyanin biosynthesis and reduction of oxidative phenomena. In conclusion, the fruit-zone cooling system proved to be an invaluable tool for mitigating the adverse effects of multiple summer stresses.

A novel semi-dominant mutation in brassinosteroid signaling kinase1 increases stomatal density.

Stomata play a pivotal role in balancing CO2 uptake for photosynthesis and water loss via transpiration. Thus, appropriate regulation of stomatal movement and its formation are crucial for plant growth and survival. Red and blue light induce phosphorylation of the C-terminal residue of the plasma membrane (PM) H+-ATPase, threonine, in guard cells, generating the driving force for stomatal opening. While significant progress has been made in understanding the regulatory mechanism of PM H+-ATPase in guard cells, the regulatory components for the phosphorylation of PM H+-ATPase have not been fully elucidated. Recently, we established a new immunohistochemical technique for detecting guard-cell PM H+-ATPase phosphorylation using leaves, which was expected to facilitate investigations with a single leaf. In this study, we applied the technique to genetic screening experiment to explore novel regulators for the phosphorylation of PM H+-ATPase in guard cells, as well as stomatal development. We successfully performed phenotyping using a single leaf. During the experiment, we identified a mutant exhibiting high stomatal density, jozetsu (jzt), named after a Japanese word meaning 'talkative'. We found that a novel semi-dominant mutation in BRASSINOSTEROID SIGNALING KINASE1 (BSK1) is responsible for the phenotype in jzt mutant. The present results demonstrate that the new immunohistochemical technique has a wide range of applications, and the novel mutation would provide genetic tool to expand our understanding of plant development mediated by brassinosteroid signaling.

A Geometric Morphometrics Approach to the Study of Natural Variations and Hybrid Detection in Populations of Alnus incana (L.) Moench and Alnus rohlenae Vít, Douda and Mandák.

Landmark-based geometric morphometrics (GM) was used to examine, for the first time, spontaneous hybridization between Alnus incana (L.) Moench and Alnus rohlenae Vít, Douda and Mandák, and to assess inter- and intrapopulation variability in leaf shape, leaf size and venation in natural populations in Serbia (Western Balkans). Two geographically distant (30 km) and two close (1.2 km) populations were selected to examine hybridization. The variability in leaf shapes was assessed by canonical variate analysis and linear discriminant analysis performed on the symmetric component of variation. Covariation between the symmetric component of shape variation and the number of pairs of secondary leaf veins was investigated with partial least squares analysis. Static allometry was examined for the first time in the genus Alnus Mill. A higher proportion of A. incana leaves was classified as A. rohlenae in geographically close populations, which is in accordance with the hypothesis about spontaneous hybridization. No single leaf of A. rohlenae was classified as A. incana, indicating that putative hybrids can only be found in grey alder populations. This study demonstrates that GM is a powerful tool for species delimitation and hybrid detection in the genus Alnus and it can be used for preliminary screening in hybrid zones.